An Architecture Framework for Complex Data Warehouses

Now-a-days, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multi-structure, multisource, multimodal, and/or multiversion. The authors term such data complex data. Managing and analyzing complex data involves a lot of different issues regarding their structure, storage and processing, and metadata are a key element in all these processes. Such problems have been addressed by classical data warehousing (i.e., applied to \"Simple\" data). However, data warehousing approaches need to be adapted for complex data.